 Hello my name is Sarah Harper and today I'm going to tell you a little bit about some work that I've been involved in at the Institute for Oceans and Fishery that really deals with how to sell fisheries data gaps, especially in small scale fisheries. Jumping right into some of the data challenges, the way that fisheries data is collected in many countries and contacts overlooks the many roles and activities that contribute to the fisheries related economy, often missing data from small scale informal unpaid and part time activities that many, many people around the world do in fisheries. National level data is also often aggregated by sector, so things like forestry with fisheries and agriculture, small scale with large scale sectors, fisheries with aquaculture and this makes it very hard from a policy perspective to understand the contributions of specific subsectors and especially the small scale sector. There's also issues of comparability across scales and contexts with different definitions and methodologies used for data collection. Standardized data is definitely lacking and very much needed. So how do we overcome some of these challenges? So I'm going to highlight a few projects that I've been involved with in the past, estimating small scale fisheries patches, counting women and then some of the methodologies used to estimate uncertainty. So small scale fisheries have historically been overlooked from a policy perspective and although there's increasing recognition of the contribution of this fishery subsector to things like food and livelihood security, small scale fisheries are notoriously data poor. There's considerable variability in what counts as fishing and how we define small scale fishing and with no universally accepted or appropriate definition of small scale fisheries each country really needs to come up with their own definition of small scale and what's appropriate to their context. So with this greater emphasis on the importance of small scale fisheries efforts to estimate the contribution of the subsector are mounting. Over the past 10 years I've been involved with projects with two working groups at the University of British Columbia, the Sea Around us project and the Fisheries Economics Research Unit whose researchers including myself have developed global data sets with national level indicators of catch and associated land and value, economic impact, employment, subsidies, all by the various sectors and subsectors of fisheries for all maritime fishing countries of the world. And this really drew on approaches that utilized all available data, often unconventional data sources that were validated through expert opinion. We use benefit transfer approach to fill data gaps. And this was very much an iterative process of estimating verifying and refining those estimates to come up with the best available estimate in these data poor context. So another major gap in fisheries data is the significant but often overlooked role of women in fisheries whose contribution to fisheries related economies are very much undervalued. This is changing, however, it's quite slow. National level fisheries data are rarely desegregated by sex and are often missing the contributions by women partly because the definition of fishing is very narrow and doesn't necessarily include all those activities informal and paid that women are involved in, especially in small scale subsectors. Some countries do a better job than others at estimating these data, but an overall effort is needed to include gender indicators that highlight gender dimensions of fisheries in census data and other data collection methodologies. This as I said is changing and partly this is because of the emphasis at the international policy level on gender equality. So this is through, for example, the small scale fisheries guidelines that have been developed by the FAO in conjunction with civil society and also the sustainable development goals that highlight gender or identify gender equality as one of their main goals, goal number five. And so these have really pushed for these dimensions to be added in a fisheries context. So finally filling fisheries data gaps of a briefly highlighted often relies on a variety of unconventional data sources, which can introduce quite a bit of uncertainty into the estimates. So this uncertainty can be partially captured using methods that have been developed in other data poor contexts. So example, for example, in estimating estimates around climate change, the IPCC used a method to score the quality of data sources used. And this approach has been adopted in a fisheries context by the C around us and other working groups. And it uses sort of a semi quantitative approach of a four point scoring system, which is based on the level of agreement and rigorousness of data sources. And then this score is translated into a metric that's used to calculate confidence intervals. And in the examples that I gave above, we used a Monte Carlo simulation to generate these estimates of lower and upper confidence intervals. And however, there's other approaches that could be used to do this type of thing. So there was just a few examples of where data gaps in fisheries exist and some approaches to filling them. Obviously, there's quite a lot more to cover here, but I'm looking forward to the discussion and some questions that we'll follow after the other two presentations. So thank you for your time. Thanks for listening. And I'll look forward to your questions in a bit.